Abstract

Effective software development relies on accurately identifying system requirements, which form the foundation for subsequent activities in the software life cycle (SLC). Proper classification of these requirements is crucial for subsequent design and implementation stages and can be achieved manually or through automated means. Machine Learning (ML) algorithms have emerged as valuable tools for addressing challenges in requirement engineering, such as identifying and prioritizing requirements. This paper examines ten studies to analyze the applied algorithms, presenting their training processes and evaluation metrics. The analysis aims to assist software engineers and researchers in selecting suitable requirement classification techniques by providing a detailed overview of various machine learning approaches and their effectiveness. Although the study references ten papers, it focuses on five key papers to demonstrate the results.

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